Hepatocellular carcinoma: Review of disease and tumor biomarkers.

© The Author(s) 2016.Hepatocellular carcinoma (HCC) is a common malignancy and now the second commonest global cause of cancer death. HCC tumorigenesis is relatively silent and patients experience late symptomatic presentation. As the option for curative treatments is limited to early stage cancers, diagnosis in non-symptomatic individuals is crucial. International guidelines advise regular surveillance of high-risk populations but the current tools lack sufficient sensitivity for early stage tumors on the background of a cirrhotic nodular liver. A number of novel biomarkers have now been suggested in the literature, which may reinforce the current surveillance methods. In addition, recent metabonomic and proteomic discoveries have established specific metabolite expressions in HCC, according to Warburgs phenomenon of altered energy metabolism. With clinical validation, a simple and non-invasive test from the serum or urine may be performed to diagnose HCC, particularly benefiting low resource regions where the burden of HCC is highest

Improving the Representation of Cross-Boundary Transport of Anthropogenic Pollution in East Asia Using Radon-222.

We report on 10 years of hourly atmospheric radon, CO, and SO2 observations at Gosan Station, Korea. An improved radon detector was installed during this period and performance of the detectors is compared. A technique is developed whereby the distribution of radon concentrations from a fetch region can be used to select air masses that have consistently been in direct contact with land-based emissions, and have been least diluted en route to the measurement site. Hourly radon concentrations are used to demonstrate and characterise contamination of remote-fetch pollution observations by local emissions at this key WMO GAW site, and a seasonally-varying 5-hour diurnal sampling window is proposed for days on which diurnal cycles are evident to minimise these effects. The seasonal variability in mixing depth and “background” pollutant concentrations are characterised. Based on a subset of observations most representative of the important regional fetch areas for this site, and least affected by local emissions, seasonal estimates of CO and SO2 in air masses originating from South China, North China, Korea and Japan are compared across the decade of observations. 2016, © Taiwan Association for Aerosol Researc

Identifying Boosted Objects with N-subjettiness

We introduce a new jet shape -- N-subjettiness -- designed to identify boosted hadronically-decaying objects like electroweak bosons and top quarks. Combined with a jet invariant mass cut, N-subjettiness is an effective discriminating variable for tagging boosted objects and rejecting the background of QCD jets with large invariant mass. In efficiency studies of boosted W bosons and top quarks, we find tagging efficiencies of 30% are achievable with fake rates of 1%. We also consider the discovery potential for new heavy resonances that decay to pairs of boosted objects, and find significant improvements are possible using N-subjettiness. In this way, N-subjettiness combines the advantages of jet shapes with the discriminating power seen in previous jet substructure algorithms.Comment: 26 pages, 26 figures, 2 tables; v2: references added; v3: discussion of results extende

Jet Substructure Without Trees

We present an alternative approach to identifying and characterizing jet substructure. An angular correlation function is introduced that can be used to extract angular and mass scales within a jet without reference to a clustering algorithm. This procedure gives rise to a number of useful jet observables. As an application, we construct a top quark tagging algorithm that is competitive with existing methods.Comment: 22 pages, 16 figures, version accepted by JHE

Analysis of multicenter clinical trials with very low event rates

INTRODUCTION: In a five-arm randomized clinical trial (RCT) with stratified randomization across 54 sites, we encountered low primary outcome event proportions, resulting in multiple sites with zero events either overall or in one or more study arms. In this paper, we systematically evaluated different statistical methods of accounting for center in settings with low outcome event proportions. METHODS: We conducted a simulation study and a reanalysis of a completed RCT to compare five popular methods of estimating an odds ratio for multicenter trials with stratified randomization by center: (i) no center adjustment, (ii) random intercept model, (iii) Mantel-Haenszel model, (iv) generalized estimating equation (GEE) with an exchangeable correlation structure, and (v) GEE with small sample correction (GEE-small sample correction). We varied the number of total participants (200, 500, 1000, 5000), number of centers (5, 50, 100), control group outcome percentage (2%, 5%, 10%), true odds ratio (1, > 1), intra-class correlation coefficient (ICC) (0.025, 0.075), and distribution of participants across the centers (balanced, skewed). RESULTS: Mantel-Haenszel methods generally performed poorly in terms of power and bias and led to the exclusion of participants from the analysis because some centers had no events. Failure to account for center in the analysis generally led to lower power and type I error rates than other methods, particularly with ICC = 0.075. GEE had an inflated type I error rate except in some settings with a large number of centers. GEE-small sample correction maintained the type I error rate at the nominal level but suffered from reduced power and convergence issues in some settings when the number of centers was small. Random intercept models generally performed well in most scenarios, except with a low event rate (i.e., 2% scenario) and small total sample size (n ≤ 500), when all methods had issues. DISCUSSION: Random intercept models generally performed best across most scenarios. GEE-small sample correction performed well when the number of centers was large. We do not recommend the use of Mantel-Haenszel, GEE, or models that do not account for center. When the expected event rate is low, we suggest that the statistical analysis plan specify an alternative method in the case of non-convergence of the primary method

Arteriolar neuropathology in cerebral microvascular disease

\ua9 2022 The Authors. Neuropathology and Applied Neurobiology published by John Wiley & Sons Ltd on behalf of British Neuropathological Society. Cerebral microvascular disease (MVD) is an important cause of vascular cognitive impairment. MVD is heterogeneous in aetiology, ranging from universal ageing to the sporadic (hypertension, sporadic cerebral amyloid angiopathy [CAA] and chronic kidney disease) and the genetic (e.g., familial CAA, cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy [CADASIL] and cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy [CARASIL]). The brain parenchymal consequences of MVD predominantly consist of lacunar infarcts (lacunes), microinfarcts, white matter disease of ageing and microhaemorrhages. MVD is characterised by substantial arteriolar neuropathology involving ubiquitous vascular smooth muscle cell (SMC) abnormalities. Cerebral MVD is characterised by a wide variety of arteriolar injuries but only a limited number of parenchymal manifestations. We reason that the cerebral arteriole plays a dominant role in the pathogenesis of each type of MVD. Perturbations in signalling and function (i.e., changes in proliferation, apoptosis, phenotypic switch and migration of SMC) are prominent in the pathogenesis of cerebral MVD, making ‘cerebral angiomyopathy’ an appropriate term to describe the spectrum of pathologic abnormalities. The evidence suggests that the cerebral arteriole acts as both source and mediator of parenchymal injury in MVD

Characterization of Fine Particulate Matter and Associations between Particulate Chemical Constituents and Mortality in Seoul, Korea

Background: Numerous studies have linked fine particles [≤ 2.5 µm in aerodynamic diameter (PM2.5)] and health. Most studies focused on the total mass of the particles, although the chemical composition of the particles varies substantially. Which chemical components of fine particles that are the most harmful is not well understood, and research on the chemical composition of PM2.5 and the components that are the most harmful is particularly limited in Asia